000087186 001__ 87186
000087186 005__ 20190316233749.0
000087186 037__ $$aREP_WORK
000087186 245__ $$aAn Adaptive Total Variation Model for Image Segmentation
000087186 269__ $$a2005
000087186 260__ $$c2005$$aEcublens
000087186 336__ $$aReports
000087186 500__ $$aITS
000087186 520__ $$aIn our previous work, tracking the iso-level sets through total variation scale-space proved to be a very efficient tool for unsupervised segmentation. Stepping on these results, we propose a new segmentation approach in a unified total variation framework. The main idea is to use the total variation energy at each scale to drive the region merging process. We show that this total variation formulation, which was originally proposed for restoration and enhancement, is also well suited for segmentation. In addition, this energy functional can be derived from a Bayesian principle using a Markov random field prior. We demonstrate the effectiveness of our method on gray scale, noisy, color and texture images.
000087186 6531_ $$aBayesian model
000087186 6531_ $$aEnergy Minimization
000087186 6531_ $$aLTS2
000087186 6531_ $$aMulti-resolution.
000087186 6531_ $$aRegion Merging
000087186 6531_ $$aSpatially Adaptive Segmentation
000087186 6531_ $$aTotal Variation Diffusion
000087186 6531_ $$aTotal Variation Regularization
000087186 6531_ $$aUnsupervised Segmentation
000087186 700__ $$aPetrovic, A.
000087186 700__ $$g120906$$aVandergheynst, P.$$0240428
000087186 8564_ $$uhttps://infoscience.epfl.ch/record/87186/files/Petrovic2005_1402.pdf$$zn/a$$s509995
000087186 909C0 $$xU10380$$0252392$$pLTS2
000087186 909CO $$ooai:infoscience.tind.io:87186$$qGLOBAL_SET$$pSTI$$preport
000087186 937__ $$aEPFL-REPORT-87186
000087186 970__ $$aPetrovic2005_1402/LTS
000087186 973__ $$sPUBLISHED$$aEPFL
000087186 980__ $$aREPORT